Hi,

A bit of context first. Recently, I fitted an autoregressive model to simulated data. The MCMC converged (R-hats below 1.01, no divergent transitions, etc.). However, all the k pareto values where above 0.7 when using `loo`

. The reason for this was:

With help, I used adaptive quadrature in the univariate version of the model. Specifically, I integrated over the parameter `log_lambda`

, which is the log latent abundance for one species at each time.

Now, I want to expand it to a multivariate model. In this case, `log_lambda`

is a vector of latent abundances at any time step.

This is a simple version of the multivariate model

```
data {
int<lower=1> S; // Number of Species
int<lower=1> T; // Number of years
int Y[T,S]; // The outcome matrix
}
parameters {
vector[S] alpha; // Vector of intrinsic growth rates
matrix[S,S] beta; // matrix with competitive parameter
vector[S] log_lambda[T]; // latent parameters
cholesky_factor_corr[S] L_Rho;
vector<lower=0>[S] sde;
}
transformed parameters{
matrix[S,S] L_S;
L_S = diag_pre_multiply(sde, L_Rho);
}
model {
// the priors
alpha ~ normal(0,1);
to_vector(beta) ~ normal(0,1);
L_Rho ~ lkj_corr_cholesky(2);
sde ~ exponential(1);
//Latent true expected abundance
log_lambda[1] ~ normal(0,5);
for(t in 2:T){
log_lambda[t]~multi_normal_cholesky(alpha +
beta*
log_lambda[t-1],
L_S);
}
//The likelihood
for(s in 1:S){
target += poisson_log_lpmf(Y[,s] | log_lambda[1:T,s]);
}
}
generated quantities{
matrix[S, S]Rho;
matrix[S, S] Sigma;
vector[T] log_lik;
vector[S] log_lambda_pred[T];
int<lower=0> Y_pred[T,S];
Rho = multiply_lower_tri_self_transpose(L_Rho);
Sigma = L_S*L_S';
log_lambda_pred[1] = log_lambda[1];
for(n in 2:T){
log_lambda_pred[n] = multi_normal_cholesky_rng(alpha + beta*
log_lambda[n-1],
L_S);
}
for(s in 1:S){
for(n in 1:T){
if(log_lambda_pred[n,s]>20){
Y_pred[n,s] = poisson_log_rng(20);
}else{
Y_pred[n,s] = poisson_log_rng(log_lambda_pred[n,s]);
}
log_lik[n] = poisson_log_lpmf(Y[n] | log_lambda[n]);
}
}
}
```

However, I am not sure how to integrate over `log_lambda`

. I guess I cannot use `integrate_1d`

as it integrates over a 1 dimensional parameter, right? However, I was wondering if there is a function similar to integrate_1d for a multivariate case. If PSIS-LOO and integrated LOO does not work for this case, what could I use for model selection and predictive accuracy? Bayes factor?

This is what I have attempted so far with integrate_1d

```
functions{
real integrand(real log_lambda,
real notused,
array[] real theta,
array[] real log_lambda_i,
array[] int yi) {
real alpha = theta[1] ;
real beta = theta[2] ;
real S = theta[3];
matrix[S,S] L_S = theta[4];
return exp(multi_normal_cholesky_lpdf(log_lambda|alpha+beta*to_row_vector(log_lambda_i), L_S) + poisson_log_lpmf(yi|log_lambda));
}
}
....
generated quantities{
....
log_lik[1] = poisson_log_lpmf(Y[1] | log_lambda[1]);
for(n in 2:T){
log_lik[n] = log(integrate_1d(integrand,
negative_infinity(),
positive_infinity(),
{alpha,beta,S,L_S}, {log_lambda[n-1]},
{y[n]},1e-8));
}
....
}
```

With the corresponding error

```
Semantic error in 'C:/Users/alfon/AppData/Local/Temp/Rtmp8cWFG6/model-76801f4252b.stan', line 10, column 15 to column 16:
-------------------------------------------------
8: real beta = theta[2] ;
9: real S = theta[3];
10: matrix[S,S] L_S = theta[4];
^
11: return exp(multi_normal_cholesky_lpdf(log_lambda|alpha+beta*to_row_vector(log_lambda_i), L_S) + poisson_log_lpmf(yi|log_lambda));
12: }
-------------------------------------------------
Matrix row size must be of type int. Instead found type real.
mingw32-make.exe: *** [make/program:50: C:\Users\alfon\AppData\Local\Temp\Rtmp8cWFG6\model-76801f4252b.hpp] Error 1
Error: An error occured during compilation! See the message above for more information.
```